"Data migration is not just about moving data from one place to another; it should be focused on: realizing all the benefits promised by the new system when you entertained the concept of new software in the first place; creating the improved enterprise performance that was the driver for the project; importing the best, the most appropriate and the cleanest data you can so that you enhance business intelligence; maintaining all your regulatory, legal and governance compliance criteria; staying securely in control of the project."
"Are data quality and data governance the same thing? They share the same goal, essentially striving for the same outcome of optimizing data and information results for business purposes. Data governance plays a very important role in achieving high data quality. It deals primarily with orchestrating the efforts of people, processes, objectives, technologies, and lines of business in order to optimize outcomes around enterprise data assets. This includes, among other things, the broader cross-functional oversight of standards, architecture, business processes, business integration, and risk and compliance. Data governance is an organizational structure that oversees the compliance and standards of enterprise data." (Neera Bhansali, "Data Governance: Creating Value from Information Assets", 2014)
"Data governance is about putting people in charge of fixing and preventing data issues and using technology to help aid the process. Any time data is synchronized, merged, and exchanged, there have to be ground rules guiding this. Data governance serves as the method to organize the people, processes, and technologies for data-driven programs like data quality; they are a necessary part of any data quality effort." (Neera Bhansali, "Data Governance: Creating Value from Information Assets", 2014)
"Data governance is the process of creating and enforcing standards and policies concerning data. [...] The governance process isn't a transient, short-term project. The governance process is a continuing enterprise-focused program." (Neera Bhansali, "Data Governance: Creating Value from Information Assets", 2014)
"Understanding an organization's current processes and issues is not enough to build an effective data governance program. To gather business, functional, and technical requirements, understanding the future vision of the business or organization is important. This is followed with the development of a visual prototype or logical model, independent of products or technology, to demonstrate the data governance process. This business-driven model results in a definition of enterprise-wide data governance based on key standards and processes. These processes are independent of the applications and of the tools and technologies required to implement them. The business and functional requirements, the discovery of business processes, along with the prototype or model, provide an impetus to address the "hard" issues in the data governance process." (Neera Bhansali, "Data Governance: Creating Value from Information Assets", 2014)
"A big part of data governance should be about helping people (business and technical) get their jobs done by providing them with resources to answer their questions, such as publishing the names of data stewards and authoritative sources and other metadata, and giving people a way to raise, and if necessary escalate, data issues that are hindering their ability to do their jobs. Data governance helps answer some basic data management questions." (Mike Fleckenstein & Lorraine Fellows, "Modern Data Strategy", 2018)
"Data lake is an ecosystem for the realization of big data analytics. What makes data lake a huge success is its ability to contain raw data in its native format on a commodity machine and enable a variety of data analytics models to consume data through a unified analytical layer. While the data lake remains highly agile and data-centric, the data governance council governs the data privacy norms, data exchange policies, and the ensures quality and reliability of data lake." (Saurabh Gupta et al, "Practical Enterprise Data Lake Insights", 2018)
"Data governance policies must not enforce constraints on data - Data governance intends to control the level of democracy within the data lake. Its sole purpose of existence is to maintain the quality level through audits, compliance, and timely checks. Data flow, either by its size or quality, must not be constrained through governance norms. [...] Effective data governance elevates confidence in data lake quality and stability, which is a critical factor to data lake success story. Data compliance, data sharing, risk and privacy evaluation, access management, and data security are all factors that impact regulation." (Saurabh Gupta et al, "Practical Enterprise Data Lake Insights", 2018)
"Data governance presents a clear shift in approach, signals a dedicated focus on data management, distinctly identifies accountability for data, and improves communication through a known escalation path for data questions and issues. In fact, data governance is central to data management in that it touches on essentially every other data management function. In so doing, organizational change will be brought to a group is newly - and seriously - engaging in any aspect of data management." (Mike Fleckenstein & Lorraine Fellows, "Modern Data Strategy", 2018)
"Data is owned by the enterprise, not by systems or individuals. The enterprise should recognize and formalize the responsibilities of roles, such as data stewards, with specific accountabilities for managing data. A data governance framework and guidelines must be developed to allow data stewards to coordinate with their peers and to communicate and escalate issues when needed. Data should be governed cooperatively to ensure that the interests of data stewards and users are represented and also that value to the enterprise is maximized." (Mike Fleckenstein & Lorraine Fellows, "Modern Data Strategy", 2018)
"Data swamp, on the other hand, presents the devil side of a lake. A data lake in a state of anarchy is nothing but turns into a data swamp. It lacks stable data governance practices, lacks metadata management, and plays weak on ingestion framework. Uncontrolled and untracked access to source data may produce duplicate copies of data and impose pressure on storage systems." (Saurabh Gupta et al, "Practical Enterprise Data Lake Insights", 2018)
"Typically, a data steward is responsible for a data domain (or part of a domain) across its life cycle. He or she supports that data domain across an entire business process rather than for a specific application or a project. In this way, data governance provides the end user with a go-to resource for data questions and requests. When formally applied, data governance also holds managers and executives accountable for data issues that cannot be resolved at lower levels. Thus, it establishes an escalation path beginning with the end user. Most important, data governance determines the level - local, departmental or enterprise - at which specific data is managed. The higher the value of a particular data asset, the more rigorous its data governance." (Mike Fleckenstein & Lorraine Fellows, "Modern Data Strategy", 2018)
"Broadly speaking, data governance builds on the concepts of governance found in other disciplines, such as management, accounting, and IT. Think of it as a set of practices and guidelines that define the loci of accountability and responsibility related to data within the organization. These guidelines support the organization's business model through generating and consuming data." (Gregory Vial, "Data Governance in the 21st-Century Organization", 2020)
"Good [data] governance requires balance and adjustment. When
done well, it can fuel digital innovation without compromising security." (Gregory
Vial, "Data Governance in the 21st-Century Organization", 2020)
"Good data governance ensures that downstream negative
effects of poor data are avoided and that subsequent reports, analyses and
conclusions are based on reliable, trusted data." (Robert F Smallwood, "Information
Governance: Concepts, Strategies and Best Practices" 2ndEd., 2020)
"Where data governance really takes place is between
strategy and the daily management of operations. Data governance should be a
bridge that translates a strategic vision acknowledging the importance of data
for the organization and codifying it into practices and guidelines that
support operations, ensuring that products and services are delivered to
customers."
"In an era of machine learning, where data is likely to be used to train AI, getting quality and governance under control is a business imperative. Failing to govern data surfaces problems late, often at the point closest to users (for example, by giving harmful guidance), and hinders explainability (garbage data in, machine-learned garbage out)." (Jesús Barrasa et al, "Knowledge Graphs: Data in Context for Responsive Businesses", 2021)
"Data governance refers to aligning all aspects of data strategy, business strategy, and compliance requirements. A three-pronged approach of people, policy, and process will provide oversight for all data operations from the time data touches a system to the point it leaves. Roles and responsibilities dictate who has access to what data, something that needs to be enforced and monitored. Data lineage is tracked to provide accountability for how data has been transformed at various steps. Delta's history functionality provides a good audit trail. A central catalog builds on top of it and provides a central place for defining the rules, enforcing them, and monitoring compliance via audit logs. Some of these catalogs have to be built and stitched together unless a managed platform that has taken care of these aspects is leveraged." (Anindita Mahapatra, "Simplifying Data Engineering and Analytics with Delta", 2022)
"Data governance creates access controls between the data product producer and consumer and provides metadata like schema definitions and lineages. In some cases, mastered data along with reference data may be relevant to the implementation. Data governance allows us to create appropriate access controls for these resources as well." (Hubert Dulay & Stephen Mooney, "Streaming Data Mesh", 2023)
"Data governance is a set of policies, standards, processes, roles, and responsibilities that collectively ensure accountability and ownership of data across the business. Policies are the rules and regulations surrounding data defined by the business itself or, more importantly, externally by laws that, if broken, could cost a business a massive amount in fines. These policies also include enforcement of standards that enable interoperability and consumability of data between domains, especially in a decentralized data platform like a streaming data mesh. These policies are implemented as processes and controls on data by authorizing, authenticating, and safeguarding private or personal data. Policies are implemented using roles that represent groups, people, or systems to create access controls around data." (Hubert Dulay & Stephen Mooney, "Streaming Data Mesh", 2023)
"Data management is the process of developing, implementing, and monitoring systems, procedures, and practices to deliver and enhance the value of data and assets throughout their lifecycle, while data and AI governance is defined as the exercise of authority and control during the management of data and assets." (Eberhard Hechler et al, "Data Fabric and Data Mesh Approaches with AI", 2023)
"Definition of data and AI governance policies, rules, and classifications is critical to break down data silos, allow for a uniform data consumption, and prevent misuse of data. It includes monitoring of compliance and enforcement of data and AI rules and policies on an ongoing basis, as well as ensuring compliance with regulations and laws." (Eberhard Hechler et al, "Data Fabric and Data Mesh Approaches with AI", 2023)
"Since domains are used to create data products, and sharing data products across many domains ultimately builds a mesh of data, we need to ensure that the data being served follows some guidelines. Data governance involves creating and adhering to a set of global rules, standards, and policies applied to all data products and their interfaces to ensure a collaborative and interoperable data mesh community. These guidelines must be agreed upon among the participating data mesh domains." (Hubert Dulay & Stephen Mooney, "Streaming Data Mesh", 2023)
"The Data Fabric architecture can help enterprises address the challenges of data and AI governance effectively, including the orchestration and exchange of metadata across organizational implementations. First, Data Fabric pulls data from disparate data sources and orchestrates metadata exchange across organizational systems, thus providing a holistic view of data and AI at the enterprise level, which lays a solid technology foundation for a consistent and unified enterprise-level data and AI governance. Likewise, a Data Fabric architecture serves as a foundation for a Data Mesh solution, which is supporting organizational or departmental data and AI governance initiatives. Second, the advanced automation and AI technologies employed by a Data Fabric architecture can greatly simplify the implementation of data and AI governance at the enterprise or organizational level, enabling organizational federated Data Mesh initiatives, where orchestration and exchange of metadata across organizations need to be implemented as well." (Eberhard Hechler et al, "Data Fabric and Data Mesh Approaches with AI", 2023)
"The term data governance is used for the processes and responsibilities that define, manage, and enforce access, privacy, availability, and security of the organization’s data. It typically includes a set of policies, rules, and data classifications and functionality to monitor and enforce compliance. As stated earlier, we use the term AI governance in a broader sense, also including AI artefacts." (Eberhard Hechler et al, "Data Fabric and Data Mesh Approaches with AI", 2023)
"Data mesh fundamentally reframes data governance and validation by distributing accountability to domain-oriented teams who act as custodians and producers of their respective data products. These teams possess intimate domain knowledge, which is essential for nuanced validation criteria that adapt to the semantics, context, and evolution of their datasets. By treating datasets as first-class products with clear ownership, interfaces, and service-level objectives, data mesh encourages autonomous validation workflows embedded directly within the domains where data originates and is consumed." (William Smith, "Great Expectations for Modern Data Quality: The Complete Guide for Developers and Engineers", 2025)
"Governance sets the strategic framework, stewardship bridges strategy with execution, and operational ownership grounds responsibility within systems and processes. Advanced organizations achieve sustainable data quality by establishing clear roles, defined escalation channels, embedded tooling, standardized processes, and a culture that prioritizes data excellence as a collective, enforceable mandate." (William Smith, "Great Expectations for Modern Data Quality: The Complete Guide for Developers and Engineers", 2025)
"The problem with data lakes is that they have several drawbacks preventing them from being the perfect or ideal solution. The first drawback is an organizational problem: (•) How to organize data in the lake (•) How to classify, catalog, secure, document, and find it (•) How to avoid the lake turning into a swamp where data is mixed, duplicated, obsolete, or inaccessible (•) How to manage quality, governance, and traceability in the lake."(Christopher Maneu et al, "The Definitive Guide to Microsoft Fabric From discovery to building a unified, secure, and scalable data platform", 2025)
"Governance requires a really fine balance - governing to the point where consistency is assured, but flexibility remains. There is no perfect formula, but finding the right governance level within your organization’s culture is a critical component to making the most of BI opportunities." (Mike Saliter)
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